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Learning techniques outperformed traditional techniques in terms of overall average, but not in terms
of overall ranking. We also found that a support vector machine (SVM) trained on multiple demand
series produced the most accurate forecasts.
introduction
Supply chain integration looks to combine resources in order to provide value to the end consumer by
improving the flow and quality of information being passed between the participants in the chain (Zhao,
Xie, & Wei, 2002). Thus, in an idealized case, where all participants adopt the integration philosophy
and make efforts to implement it fully, the entire chain would perform effectively and efficiently in
responding to end customer demands. However, although integration and sharing information can po-
tentially reduce forecast errors, in reality they are neither ubiquitous nor complete and demand forecast
errors still abound.
This is due to the fact that the original demand signal becomes distorted as it travels through the
extended supply chain (a holistic notion of supply chain (Tan, 2001) that requires collaborative relation-
ships (Davis & Spekman, 2004)). Demand forecast quality can be improved if done cooperatively by the
partners in the chain. Collaborative forecasting and replenishment (CFAR) permits a firm and its sup-
plier-firm to coordinate decisions by exchanging complex decision-support models and strategies, thus
facilitating integration of forecasting and production schedules (Raghunathan, 1999). In the absence of
CFAR, firms are relegated to traditional forecasting and production scheduling, a challenging task due
to what the well-known phenomenon of “bullwhip effect” (Lee, Padmanabhan, & Whang, 1997a).
The value of information sharing across the supply chain is widely recognized as the means of
combating demand signal distortion (Lee, Padmanabhan, & Whang, 1997b). However, there is a gap
between the ideal of integrated supply chains and reality (Gunasekaran & Ngai, 2004).
Researchers have identified several factors that could hinder such long-term stable collaborative efforts.
Premkumar (2000) lists some required critical issues that must be addressed to permit successful supply
chain collaboration, including: (i) alignment of business interests, (ii) long-term relationship manage-
ment, (iii) reluctance to share information, (iv) complexity of large-scale supply chain management, (v)
competence of personnel supporting supply chain management and (vi) performance measurement and
incentive systems to support supply chain management. Although these are important issues, in many
companies, these issues have not yet been addressed in attempts to enable effective extended supply chain
collaboration (Davis & Spekman, 2004). Additionally, in many supply chains there are power regimes
and power sub-regimes that can prevent supply chain optimization (Cox, Sanderson, & Watson, 2001).
The introduction of inaccurate information into the system could also lead to demand distortion, e.g.,
double forecasting and ration gaming by the partners, ordering more quantities than needed, despite the
presence of a collaborative system and an incentive towards its usage (Heikkila, 2002).
Furthermore, the globalization trends and the advance of E-business increase the tendency towards
more “dynamic” (Vakharia, 2002) and “agile” (Gunasekaran & Ngai, 2004; Yusuf, Gunasekaran, Adeleye,
& Sivayoganathan, 2004) supply chains. While this trend enables the supply chains to be more flexible
and adaptive, it could discourage companies from investing in long-term collaborative relationships
among each other due to the restrictive nature of such commitments. The over-emphasis on investing in
extensive relationships among the partners could lead to a “lock-in” situation, thus seriously jeopardizing
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